This study explores the role of perceived utility, social influence, and ethical concerns in the adoption of AI-based data analysis tools among academic researchers in China, focusing on differences between public and private universities. The research aims to identify key drivers and barriers influencing the integration of AI technology in academic settings. A quantitative approach was employed, using a multi-group structural equation model (SEM) analysis to assess data collected from 750 academic researchers across various disciplines (N[subscript pvt] = 402; N[subscript pub] = 348). The findings reveal that both perceived utility and social influence significantly influence the adoption of AI tools. Higher perceived utility and stronger social influence lead to greater adoption. However, ethical concerns were found to moderate these relationships, particularly in public universities, where researchers with high ethical concerns perceived greater risks, thereby reducing their likelihood of adoption. In contrast, private university researchers showed a higher tolerance for perceived risks when utility and social influence were evident. The study's implications suggest that to promote AI adoption, institutions must address ethical concerns and perceived risks, particularly in public universities, by enhancing transparency, providing ethical guidelines, and offering comprehensive training. These efforts can lead to more effective integration of AI technologies, ultimately enhancing research productivity and innovation across diverse academic environments.